83% vs 39%: Why China Loves AI, America Fears It — and Who's Right

 This week in AI

Last Thursday, a Chinese startup you've probably never heard of did something remarkable. Moonshot AI released Kimi K3, the largest open-weight AI model ever built — 2.8 trillion parameters, which is a fancy way of saying it's enormous. On some benchmarks it outperforms the best models from OpenAI and Anthropic, and it runs at about a third of Anthropic's price (Prof G Media, 2026, 00:00:44). Even more remarkable: unlike American frontier models, which are locked behind corporate walls, Kimi K3's weights are free. Anyone can download it, study it, and build on it.

Babel or Jerusalem? What the Pope Gets Right — and Half-Wrong — About AI

Pope Leo XIV just wrote Magnifica Humanitas, a 40,000 words Encyclical Letter about artificial intelligence. He compares Silicon Valley to the Tower of Babel, warns that algorithms are quietly deciding who gets a job or a loan, and even apologizes for the Church's own past sins. It might be the most important document about technology you'll never read — so I read it for you. And while I applaud most of it, as an economic historian I have a bone to pick with the ghost of the Luddites hovering between its lines.

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A social encyclical for the age of AI

On 15 May 2026, Pope Leo XIV published Magnifica Humanitas ("The Grandeur of Humanity"), an encyclical letter "on safeguarding the human person in the time of artificial intelligence" (Leo XIV, 2026). The date was no accident. Exactly 135 years earlier, on 15 May 1891, Pope Leo XIII published Rerum Novarum, the document that founded what we now call the Social Doctrine of the Church — the Church's body of teaching on work, the economy, and justice (Leo XIII, 1891). Back then, the "new things" (res novae) were factories, industrial capitalism, and the exploitation of workers. Today, the new pope argues, the res novae are digitalization, robotics, and AI (Leo XIV, 2026, para. 4).

Europe's Firms Are Adopting AI — But at Three Very Different Speeds

The people got there first

Something historically unusual is happening with artificial intelligence. For most general-purpose technologies, businesses led and households followed. Electric dynamos powered factories for roughly four decades before households electrified at scale, and the personal computer entered offices years before it entered living rooms (Comin & Hobijn, 2010; David, 1990). Generative AI has inverted that sequence: by August 2024, nearly 39% of the U.S. working-age population had already used it — a faster initial diffusion than either the PC or the internet at comparable points (Bick et al., 2024) — while firm-level surveys were still reporting adoption under 10% (Bonney et al., 2024). Workers, quite literally, dragged AI into their companies in their pockets.

Inside the Mind of the Machine: is AI lying to us?

 

6 Counter-Intuitive Takeaways on the Road to AGI

Standing amidst the stones of King’s Parade in Cambridge, one cannot help but feel the weight of intellectual history. It is a city where the "intellectual giants" of the past—from Charles Babbage to Alan Turing—once walked, laying the theoretical foundations for the world we now inhabit. Yet, as I sat in a historic Cambridge lecture hall listening to Demis Hassabis and later spoke with the researchers at Google DeepMind, a startling paradox became clear: we have entered an era where we can build systems of immense intelligence that we do not fully understand. We have graduated from traditional software engineering into the "Black Box" problem, creating machines that mirror human intuition more closely than they do traditional logic.

AI: The Root Node of Reality

 

The Root Node of Reality: Why Demis Hassabis Thinks AI is the Successor to Mathematics

A Homecoming to the Future

There is a profound symmetry in Demis Hassabis (Nobel prize winner in Chemistry 2024) returning to the wooden benches of his favorite Cambridge lecture hall. It was here, as an undergraduate, that he absorbed the theoretical underpinnings of computation that would eventually power DeepMind. Before the lecture, Hassabis was invited to sign the Nobel book—a ritual of scientific passage. As he leafed back through the vellum pages, he found himself staring at the signatures of Francis Crick and Albert Einstein. It was a visceral reminder that he wasn't just visiting an alma mater; he was stepping into a lineage of giants who decoded the fundamental scripts of reality.

Hassabis’s journey to this moment began not with a laboratory, but with a "lump of inanimate plastic"—his first chess computer. As a four-year-old child prodigy, he was less interested in winning than in the mystery of how a machine could be programmed to outthink a human. This "relatable curiosity" evolved into a lifelong quest to understand the "root node" of existence: intelligence itself.

Education and Its Limits

Education and Its Limits: What Daniel Susskind Gets Right — and Where I'd Push Back

Notes from Daniel Susskind's Gresham College lecture, "Education and Its Limits" (16 May 2026)

Daniel Susskind closed his Gresham College lecture series on the future of work with a talk that should land squarely in the inbox of every school leader, curriculum designer, and policy maker currently drafting an "AI strategy" (Susskind, 2026, 00:10:33). His core claim: more education remains our best response to technological disruption, but what we mean by "more education" has to change — and even a reformed version of education won't be enough on its own (Susskind, 2026, 00:16:29).

Below is my summary of the argument, followed by where I think it holds up and where I'd want to see it stress-tested.

The New Infrastructure Revolution: How We Turn AI into a Common Good

The AI-based innovation society — what can history teach us about the future?

(Italian version below)

Introduction

Every technology forces one honest question: who is this actually for? For artificial intelligence, that question is now urgent, and history is the best place to look for an answer.

My aim in this article is simple: to help people catch up with the fast-moving developments around AI without needing to wade through hours of expert commentary themselves. To do that, I draw on three recent YouTube conversations featuring some of the world's leading voices on AI and its economic impact — the economist Mariana Mazzucato [1], the Nobel laureate Geoffrey Hinton [2], and the entrepreneurs Salim Ismail and Dave Blundin [3]. Between them they represent the three positions worth understanding: govern it, fear it, and ride it.